Modeling multimedia ad exposure : the role of low-level audiovisual features

Burnik, Urban (avtor), Košir, Andrej (avtor), Strle, Gregor (avtor)

This study investigates whether low-level audio and video features can explain the variance in multimedia exposure as measured by the Multimedia Advertising Exposure Scale (MMAES). The goal is to understand the role of these features in predicting exposure and explore whether incorporating nonlinear relationships based on interactions can improve modeling. An observational study with young participants (N=287) evaluated exposure to eight video ads. Linear and polynomial regression models were used to predict MMAES scores using low-level features. Results indicated that polynomial models outperformed linear models, capturing complex interactions and providing better predictive accuracy. The best polynomial model explained 34.6% of the variance in MMAES scores, with an MAE of 0.207, and an RMSE of 0.269. Bootstrap analysis confirmed model robustness, showing lower error rates and stable coefficients for polynomial models. SHapley Additive exPlanations (SHAP) analysis highlighted key features and their interactions, improving interpretability. These findings underscore the importance of nonlinear relationships in modeling multimedia exposure, with implications for optimizing multimedia advertising strategies

• Jezik: Angleški jezik
• Ključne besede: multimedia advertising exposure, low-level audio features, low-level video features, predictive modeling, machine learning in advertising, consumer behavior analysis, multimedia singal processing
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FE - Fakulteta za elektrotehniko
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2025
• Št. strani: 20 str.
• Številčenje: Vol. 13
• PID: 20.500.12556/RUL-167430
• UDK: 621.3
• ISSN pri članku: 2169-3536
• DOI: 10.1109/ACCESS.2025.3536633
• COBISS.SI-ID: 225180931
• Datum objave v RUL: 21.02.2025

Gradivo je del revije

• Naslov: IEEE access
• Založnik: Institute of Electrical and Electronics Engineers
• ISSN: 2169-3536
• COBISS.SI-ID: 519839513

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: multimedijsko oglaševanje, izpostavljenost, zvočne značilke, video značilke, strojno učenje, analiza obnašanja potrošnikov, napovedano modeliranje, multimedijska obdelava signalov

Projekti

• Financer: ARIS - Javna agencija za znanstvenoraziskovalno in inovacijsko dejavnost Republike Slovenije
• Številka projekta: P2-0246
• Naslov: ICT4QoL - Informacijsko komunikacijske tehnologije za kakovostno življenje